GPU-Based High-Performance Imaging for Mingantu Spectral RadioHeliograph

TL;DR

This paper presents a GPU-accelerated imaging pipeline for the Mingantu Spectral RadioHeliograph, significantly enhancing processing speed and image quality for large solar radio interferometry data.

Contribution

It introduces a high-performance GPU-based imaging pipeline tailored for MUSER, including novel algorithms for solar disk detection and image processing optimization.

Findings

Processing speed increased significantly

Images meet high-quality standards

Pipeline effectively handles large data volumes

Abstract

As a dedicated solar radio interferometer, the MingantU SpEctral RadioHeliograph (MUSER) generates massive observational data in the frequency range of 400 MHz -- 15 GHz. High-performance imaging forms a significantly important aspect of MUSER's massive data processing requirements. In this study, we implement a practical high-performance imaging pipeline for MUSER data processing. At first, the specifications of the MUSER are introduced and its imaging requirements are analyzed. Referring to the most commonly used radio astronomy software such as CASA and MIRIAD, we then implement a high-performance imaging pipeline based on the Graphics Processing Unit (GPU) technology with respect to the current operational status of the MUSER. A series of critical algorithms and their pseudo codes, i.e., detection of the solar disk and sky brightness, automatic centering of the solar disk and estimation of the number of iterations for clean algorithms, are proposed in detail. The preliminary experimental results indicate that the proposed imaging approach significantly increases the processing performance of MUSER and generates images with high-quality, which can meet the requirements of the MUSER data processing.